1. Field of the Invention
The present invention relates to an automated method and apparatus for lung nodule detection in chest radiographs, and more particularly to an automated method and apparatus in which lung nodules are detected in chest radiographs with a reduction in false positive detection.
2. Discussion of the Background
X-ray chest radiography is the most commonly used radiological imaging modality for detection of solitary subtle lung nodules in patients because of the low radiation does, low cost, and reliability. Solitary lung nodules in chest images are one of the important signs of primary lung cancer, which is the leading cause of cancer death in men and women in the United States.[1,2] However, it is well known that radiologists may fail to detect lung nodules in as many as 20 to 30% of actually positive cases viewed retrospectively.[3-6] In an effort to help radiologists to improve their diagnostic accuracy, at the Department of Radiology at the University of Chicago (UC), a computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in chest images, as disclosed in above-noted U.S. patent application Ser. No. 08/562,087, has been developed.[7,8] A radiologist may use the computer output as a second opinion in making his/her diagnosis.[9,10]
The UC CAD scheme begins with a difference image technique, as disclosed in U.S. Pat. No. 4,907,156 [11], in which a nodule-suppressed image is subtracted from a nodule-enhanced image to produce a so-called difference image for reduction of normal background structures in the chest image. Nodule candidates in the chest image are selected by multiple gray-level thresholding of the difference image.[7,12] The derived nodule candidates are then classified into six groups according to the levels used by the multiple gray-level thresholding. The adaptive rule-based image feature analysis method is applied to nodule candidates in each group for removal of the corresponding false positives in each group. Finally, an artificial neural network (ANN) is trained to identify the candidates remaining after the rule-based tests.[7,8] For the UC database, which consisted of 200 PA chest images, including 100 normals and 100 abnormals (with 122 confirmed nodules), the prior UC CAD scheme achieves a performance of 70% sensitivity with 1.7 false positives per chest image.
It has been found that the majority of false-positive detections resulting from the prior UC CAD scheme are related to rib--rib or rib-vessel crossings, and that some others are due to shadows of soft tissues such as breast, heart, and diaphragm. The prior UC method for elimination of false-positive detections in the CAD scheme is based on gray levels and morphologic features obtained by the region-growing technique. These image features are derived from both the difference image and the original chest image.[7,8]